{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要的工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据文件\n",
    "train = pd.read_csv(\"./diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据集情况\n",
    "print(\"Train :\", train.shape)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pregnancies直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Pregnancies,kde = False)\n",
    "plt.xlabel('Pregnancies')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Pregnancies 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='Pregnancies', data=train,hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('Pregnancies', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Glucose直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose,kde = False)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Glucose 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='Glucose', data=train,hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BloodPressure直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure,kde = False)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BloodPressure 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='BloodPressure', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#SkinThickness直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness,kde = False)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SkinThickness 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='SkinThickness', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin,kde=False)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Insulin 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='Insulin', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('Insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pifuBe5uvt45B7hOBe5rcDwJ/3uw/Fvga8DC9YfbPdJ11nvy/CNw26pmbbPc1Xxtm/9sb9fdHk/FkYLp5j3wROGzUc9O7GOJ7wCv79u12Zu9AliQt6mkiSdKALANJkmUgSbIMJElYBpIkLANpl5K80KwGeV+Su5O8qdk/maSSfLDv2GVJnk/yiWb7L5L8cVfZpd1hGUi79lz1VoU8id5ihx/qe24z8La+7YvoXVcvjR3LQBrcofSWi571HLAxyewSzb8B3DT0VNI+0MlnIEtj5GXNKqcH0VsK+Oydnr8BuDjJ48AL9NYIevVwI0p7zzKQdu256q1ySpIzgX9O8oa+578EfBB4Arixg3zSPuE0kTSgqvovYBkw0bfvx8B64Cp6q7hKY8mRgTSgJCcAB9BbFOzgvqf+DviPqvpeb0FXafxYBtKuzZ4zgN6Hyqyqqhf6/9Gvqg14FZHGnKuWSpI8ZyBJsgwkSVgGkiQsA0kSloEkCctAkoRlIEnCMpAkAf8Pa4C97gRg/6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI,kde=False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='Insulin', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#DiabetesPedigreeFunction直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction,kde=False)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#DiabetesPedigreeFunction 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='DiabetesPedigreeFunction', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Age,kde=False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wucwu1/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Age 与 Outcome 关系\n",
    "sns.violinplot(x='Outcome', y='Age', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Outcome', fontsize=12)\n",
    "plt.ylabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4a5c02080>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#两两数据特征之间的相关程度\n",
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n",
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#将缺省值替换成NULL值\n",
    "NullCol = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NullCol] == 0).sum())\n",
    "train[NullCol] = train[NullCol].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#将NULL值替换为数据集平均值\n",
    "medians = train.median()\n",
    "train = train.fillna(medians)\n",
    "\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存数据文件\n",
    "train.to_csv('new_diabetes.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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